What is the difference between these two ways of saving keras machine learning model weights?

Question

I saw two ways of saving the weights of a keras model.

First way;

checkpointer = ModelCheckpoint(filepath=\"weights.hdf5\", verbose=1, save_best_only=Tru         


        

Answer 1

HDF5 (.h5, .hdf5)

As fundamentally explained here reference.wolfram.com/language/ref/format/HDF5.html

HDF is an acronym for Hierarchical Data Format. HDF5 is HDF Version 5.

Import["file.h5"] 

imports an HDF5 file, returning the names of the datasets stored in the file.

In Keras documents; as you can see here keras.io/api/models/model_saving_apis/

model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'

In Tensorflow documents; as you can see here tensorflow.org/tutorials/keras/save_and_load

# Save the entire model to a HDF5 file.
# The '.h5' extension indicates that the model should be saved to HDF5.
model.save('my_model.h5')

At fundamental level they are same thing. They cant create any difference.